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2-D CFAR Procedure of Multiple Target Detection for Automotive Radar

2017-09-23
Abstract In Advanced Driver Assistant System (ADAS), the automotive radar is used to detect targets or obstacles around the vehicle. The procedure of Constant False Alarm Rate (CFAR) plays an important role in adaptive targets detection in noise or clutter environment. But in practical applications, the noise or clutter power is absolutely unknown and varies over the change of range, time and angle. The well-known cell averaging (CA) CFAR detector has a good detection performance in homogeneous environment but suffers from masking effect in multi-target environment. The ordered statistic (OS) CFAR is more robust in multi-target environment but needs a high computation power. Therefore, in this paper, a new two-dimension CFAR procedure based on a combination of Generalized Order Statistic (GOS) and CA CFAR named GOS-CA CFAR is proposed. Besides, the Linear Frequency Modulation Continuous Wave (LFMCW) radar simulation system is built to produce a series of rapid chirp signals.
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2019-2020 Reviewers

2020-12-14
The SAE International Journal of Aerospace would like to thank and acknowledge the reviewers who have done peer reviews on articles over the last 2 years:
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2019-2020 Reviewers

2020-12-28
Abstract The SAE International Journal of Fuels and Lubricants would like to thank and acknowledge the reviewers who have done peer reviews on articles over the last 2 years:
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2019-2020 Reviewers

2021-02-03
Abstract The SAE International Journal of Connected and Automated Vehicles would like to thank and acknowledge the reviewers who have done peer reviews on articles over the last 2 years:
Journal Article

2019-2020 Reviewers

2020-12-29
The SAE International Journal of Electrified Vehicles would like to thank and acknowledge the reviewers who have done peer reviews on articles over the last 2 years:
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2020 Reviewers

2020-12-28
The SAE International Journal of Sustainable Transportation, Energy, Environment, & Policy would like to thank and acknowledge the reviewers who have done peer reviews on articles over the last 2 years:
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